{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence Measures for GLM Logit\n",
    "\n",
    "\n",
    "Based on draft version for GLMInfluence, which will also apply to discrete Logit, Probit and Poisson, and eventually be extended to cover most models outside of time series analysis.\n",
    "\n",
    "The example for logistic regression was used by Pregibon (1981) \"Logistic Regression diagnostics\" and is based on data by Finney (1947).\n",
    "\n",
    "GLMInfluence includes the basic influence measures but still misses some measures described in Pregibon (1981), for example those related to deviance and effects on confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "from statsmodels.genmod import families"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.stats.tests.test_influence\n",
    "test_module = statsmodels.stats.tests.test_influence.__file__\n",
    "cur_dir = cur_dir = os.path.abspath(os.path.dirname(test_module))\n",
    "\n",
    "file_name = 'binary_constrict.csv'\n",
    "file_path = os.path.join(cur_dir, 'results', file_name)\n",
    "df = pd.read_csv(file_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:              constrict   No. Observations:                   39\n",
      "Model:                            GLM   Df Residuals:                       36\n",
      "Model Family:                Binomial   Df Model:                            2\n",
      "Link Function:                  logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -14.614\n",
      "Date:                Tue, 24 Dec 2019   Deviance:                       29.227\n",
      "Time:                        14:57:33   Pearson chi2:                     34.2\n",
      "No. Iterations:                     7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const          -2.8754      1.321     -2.177      0.029      -5.464      -0.287\n",
      "log_rate        4.5617      1.838      2.482      0.013       0.959       8.164\n",
      "log_volumne     5.1793      1.865      2.777      0.005       1.524       8.834\n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "res = GLM(df['constrict'], df[['const', 'log_rate', 'log_volumne']],\n",
    "          family=families.Binomial()).fit(attach_wls=True, atol=1e-10)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get the influence measures\n",
    "\n",
    "GLMResults has a `get_influence` method similar to OLSResults, that returns and instance of the GLMInfluence class. This class has methods and (cached) attributes to inspect influence and outlier measures.\n",
    "\n",
    "This measures are based on a one-step approximation to the the results for deleting one observation. One-step approximations are usually accurate for small changes but underestimate the magnitude of large changes. Event though large changes are underestimated, they still show clearly the effect of influential observations\n",
    "\n",
    "In this example observation 4 and 18 have a large standardized residual and large Cook's distance, but not a large leverage. Observation 13 has the largest leverage but only small Cook's distance and not a large studentized residual.\n",
    "\n",
    "Only the two observations 4 and 18 have a large impact on the parameter estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "infl = res.get_influence(observed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dfb_const</th>\n",
       "      <th>dfb_log_rate</th>\n",
       "      <th>dfb_log_volumne</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.073359</td>\n",
       "      <td>-0.930200</td>\n",
       "      <td>-1.017953</td>\n",
       "      <td>0.429085</td>\n",
       "      <td>3.681352</td>\n",
       "      <td>0.086745</td>\n",
       "      <td>1.134573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.959508</td>\n",
       "      <td>-0.827905</td>\n",
       "      <td>-0.847666</td>\n",
       "      <td>0.328152</td>\n",
       "      <td>3.055542</td>\n",
       "      <td>0.095386</td>\n",
       "      <td>0.992197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.259120</td>\n",
       "      <td>0.202363</td>\n",
       "      <td>-0.004883</td>\n",
       "      <td>0.066770</td>\n",
       "      <td>-1.150095</td>\n",
       "      <td>0.131521</td>\n",
       "      <td>-0.447560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.236747</td>\n",
       "      <td>-0.194984</td>\n",
       "      <td>0.028643</td>\n",
       "      <td>0.065370</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>0.168219</td>\n",
       "      <td>0.442844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.116501</td>\n",
       "      <td>-0.099602</td>\n",
       "      <td>0.132197</td>\n",
       "      <td>0.055382</td>\n",
       "      <td>0.713771</td>\n",
       "      <td>0.245917</td>\n",
       "      <td>0.407609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.128107</td>\n",
       "      <td>0.041039</td>\n",
       "      <td>-0.100410</td>\n",
       "      <td>0.051950</td>\n",
       "      <td>-1.420261</td>\n",
       "      <td>0.071721</td>\n",
       "      <td>-0.394777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.093083</td>\n",
       "      <td>-0.009463</td>\n",
       "      <td>0.177532</td>\n",
       "      <td>0.046492</td>\n",
       "      <td>-0.847046</td>\n",
       "      <td>0.162757</td>\n",
       "      <td>-0.373465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.196119</td>\n",
       "      <td>0.127482</td>\n",
       "      <td>0.035689</td>\n",
       "      <td>0.031168</td>\n",
       "      <td>-1.065576</td>\n",
       "      <td>0.076085</td>\n",
       "      <td>-0.305786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.088174</td>\n",
       "      <td>-0.013657</td>\n",
       "      <td>-0.002161</td>\n",
       "      <td>0.027481</td>\n",
       "      <td>-1.238185</td>\n",
       "      <td>0.051031</td>\n",
       "      <td>-0.287130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.092235</td>\n",
       "      <td>0.170980</td>\n",
       "      <td>0.038080</td>\n",
       "      <td>0.026230</td>\n",
       "      <td>0.661478</td>\n",
       "      <td>0.152431</td>\n",
       "      <td>0.280520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dfb_const  dfb_log_rate  dfb_log_volumne   cooks_d  standard_resid  \\\n",
       "Case                                                                       \n",
       "4      1.073359     -0.930200        -1.017953  0.429085        3.681352   \n",
       "18     0.959508     -0.827905        -0.847666  0.328152        3.055542   \n",
       "19    -0.259120      0.202363        -0.004883  0.066770       -1.150095   \n",
       "29     0.236747     -0.194984         0.028643  0.065370        0.984729   \n",
       "31     0.116501     -0.099602         0.132197  0.055382        0.713771   \n",
       "24    -0.128107      0.041039        -0.100410  0.051950       -1.420261   \n",
       "13    -0.093083     -0.009463         0.177532  0.046492       -0.847046   \n",
       "23    -0.196119      0.127482         0.035689  0.031168       -1.065576   \n",
       "33    -0.088174     -0.013657        -0.002161  0.027481       -1.238185   \n",
       "6     -0.092235      0.170980         0.038080  0.026230        0.661478   \n",
       "\n",
       "      hat_diag  dffits_internal  \n",
       "Case                             \n",
       "4     0.086745         1.134573  \n",
       "18    0.095386         0.992197  \n",
       "19    0.131521        -0.447560  \n",
       "29    0.168219         0.442844  \n",
       "31    0.245917         0.407609  \n",
       "24    0.071721        -0.394777  \n",
       "13    0.162757        -0.373465  \n",
       "23    0.076085        -0.305786  \n",
       "33    0.051031        -0.287130  \n",
       "6     0.152431         0.280520  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summ_df = infl.summary_frame()\n",
    "summ_df.sort_values('cooks_d', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infl.plot_influence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infl.plot_index(y_var='cooks', threshold=2 * infl.cooks_distance[0].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infl.plot_index(y_var='resid', threshold=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infl.plot_index(y_var='dfbeta', idx=1, threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infl.plot_index(y_var='dfbeta', idx=2, threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infl.plot_index(y_var='dfbeta', idx=0, threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
